Robustification
Robustification is the process of making a model, estimator, or system more resistant to uncertainty, variability, and disturbance. It is used across statistics, optimization, control theory, and machine learning to improve reliability when data or conditions deviate from ideal assumptions.
In statistics, robustification aims to reduce sensitivity to outliers and model misspecification. This is typically achieved
In optimization and control, robustification involves designing formulations that perform well over a range of possible
In machine learning, robustification addresses resistance to adversarial perturbations and distributional shifts. Approaches include adversarial training,
Common challenges include trade-offs between robustness and nominal efficiency, higher computational cost, and the need to